Core Competencies for Cataloging and Metadata Professional Librarians: Assessment of Community Use and Recommendations for the Future of the Document
Bibliographic record
Abstract
The Association for Library Collections & Technical Services (ALCTS) Board of Directors approved the Core Competencies for Cataloging and Metadata Professional Librarians, hereafter referred to as the “Core Competencies,” in January 2017. The Core Competencies lists the skills required of professionals performing cataloging and metadata work in libraries of all types. In the six years since the document’s release, the cataloging and metadata community has adopted new cataloging standards, experimented with new tools, and engaged in conversations and reparative efforts around inclusive metadata. In this paper, we, the authors of the Core Competencies, report the results of our survey research that assessed the current use of the document within the cataloging and metadata community and solicited comments on ways in which the document might be revised. We conclude with recommendations for immediate changes to the document, and for its future use and maintenance.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.035 | 0.087 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".